Face recognition is a branch of pattern recognition, in which human visual perception in terms of face recognition is imitated in a computer. Face recognition has become one of the most important research areas of pattern recognition. In the last few decades, biometrics recognition has been an intensive field of research and consequently, a number of face recognition algorithms have been proposed by computer scientists, neuroscientists, and psychologists. Computer scientists attempt to develop methods for face recognition, whereas psychologists and neuroscientists typically work on the biological process of human face recognition.
Facial recognition is a popular biometric used for investigative, security, and anti-terrorism purposes due to its ease of end-user use and potential ability to identify an individual from distance. Unsupervised statistical methods such as Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Direct Linear Discriminate analysis (DLDA), Independent Component Analysis (ICA), Kernel Principal Component Analysis (KPCA), and Support Vector Machines (SVM) are the most popular face recognition algorithms. These algorithms find a set of base images and represent faces as linear combinations of those base images. However, accurate facial recognition involves several challenges. For example, different types of variabilities of facial images in different environments make facial recognition more difficult and existing facial recognition algorithms less accurate. Such variabilities, which make the face recognition more complex, include face illumination, face pose, expression, eyeglasses, makeup, etc. These variabilities have a great influence when dealing with large databases of face images using existing algorithms. As a result, two issues arise in existing face recognition algorithms, feature representation and classification based on features.
Conventional face recognition methods can be classified into two groups, face and constituent. Face-based method (appearance-based technique) uses raw information from face images, i.e., pixels. These methods include PCA-, LDA-, KPCA-, and SVM-based methods, whereas constituent-based approaches use the relationships between face features, i.e., nose, lips, and eyes. Among appearance-based representation, PCA- and LDA-based methods are the two most powerful methods for dimensionality reduction and are successfully applied in many complex classification problems such as speech recognition, face recognition, etc. In general, LDA-based methods perform better than PCA-based methods; but on the other hand, LDA-based methods face problems with Small Sample Size (SSS) and separability criteria. The conventional solution to misclassification for SSS problem and large data set with similar faces is the use of PCA into LDA, typically referred to as “Fisherfaces.” PCA is used for dimensionality reduction, and then LDA is performed on to the lower dimensional space. However, the use of LDA over PCA results in loss of significant discriminatory information.